The Deep Learning on Supercomputers workshop is with ISC'20 on June 25th, 2020 in Frankfurt, Germany. It is the fourth workshop in the Deep Learning on Supercomputers series. The workshop provides a forum for practitioners working on any and all aspects of DL for scientific research in the High Performance Computing (HPC) context to present their latest research results and development, deployment, and application experiences. The general theme of this workshop series is the intersection of DL and HPC, while the theme of this particular workshop is centered around the applications of deep learning methods in scientific research: novel uses of deep learning methods, e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial network (GAN), and reinforcement learning (RL), for both natural and social science research, and innovative applications of deep learning in traditional numerical simulation. Its scope encompasses application development in scientific scenarios using HPC platforms; DL methods applied to numerical simulation; fundamental algorithms, enhanced procedures, and software development methods to enable scalable training and inference; hardware changes with impact on future supercomputer design; and machine deployment, performance evaluation, and reproducibility practices for DL applications with an emphasis on scientific usage. Topics include but are not limited to: - DL as a novel approach of scientific computing - Emerging scientific applications driven by DL methods - Novel interactions between DL and traditional numerical simulation - Effectiveness and limitations of DL methods in scientific research - Algorithms and procedures to enhance reproducibility of scientific DL applications - DL for science workflows - Data management through the life cycle of scientific DL applications - General algorithms and procedures for efficient and scalable DL training - Scalable DL methods to address the challenges of demanding scientific applications - General algorithms and systems for large scale model serving for scientific use cases - New software, and enhancements to existing software, for scalable DL - DL communication optimization at scale - I/O optimization for DL at scale - DL performance evaluation and analysis on deployed systems - DL performance modeling and tuning of DL on supercomputers - DL benchmarks on supercomputers - Novel hardware designs for more efficient DL - Processors, accelerators, memory hierarchy, interconnect changes with impact on deep learning in the HPC context As part of the reproducibility initiative, the workshop requires authors to provide information such as the algorithms, software releases, datasets, and hardware configurations used.

Massive Open Online Courses (MOOCs) have become popular platforms for online learning. While MOOCs enable students to study at their own pace, this flexibility makes it easy for students to drop out of class. In this paper, our goal is to predict if a learner is going to drop out within the next week, given clickstream data for the current week. To this end, we present a multi-layer representation learning solution based on branch and bound (BB) algorithm, which learns from low-level clickstreams in an unsupervised manner, produces interpretable results, and avoids manual feature engineering. In experiments on Coursera data, we show that our model learns a representation that allows a simple model to perform similarly well to more complex, task-specific models, and how the BB algorithm enables interpretable results. In our analysis of the observed limitations, we discuss promising future directions.

Autoencoders are a deep learning model for transforming data from a high-dimensional space to a lower-dimensional space. They work by encoding the data, whatever its size, to a 1-D vector. This vector can then be decoded to reconstruct the original data (in this case, an image). The more accurate the autoencoder, the closer the generated data is to the original. In this tutorial we'll explore the autoencoder architecture and see how we can apply this model to compress images from the MNIST dataset using TensorFlow and Keras. The most common type of machine learning models are discriminative.

Artificial Intelligence is one of the hottest field in computer science at the moment and has taken the world by storm as a major field of development and research. Python has emerged as a dominant language in AI/ML programming because of its simplicity and flexibility. Are you a Python developer who is interested to build real-world Artificial Intelligence applications? If so, A-Z Artificial Intelligence with Python is for you! This comprehensive 5-in-1 training course is designed such that you can add an intelligence layer to any application that's based on images, text, stock market, or some other form of data.

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Get seven free days to experience our Machine Learning With SAS Viya course. Learn the theoretical foundation for different techniques associated with supervised machine learning models. You'll develop a series of supervised learning models, including decision tree, ensemble of trees (forest and gradient boosting), neural networks and support vector machines.

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Applications are invited for two University Lectureships in the broad area of Machine Learning and/or Computer Vision. The successful candidate will join the Information Engineering Division which includes the Computational and Biological Learning Laboratory and the Machine Intelligence Laboratory. The candidate will lead a research programme in one or more of the following areas: Machine Learning, Decision Making, and Computer Vision. We encourage applicants who will strengthen our current research activities in probabilistic machine learning, reinforcement learning, supervised and unsupervised learning, object recognition and detection, segmentation, tracking, and all aspects of machine intelligence. These positions have been funded in part by a generous contribution from Toyota Motor Corporation.

In this article, I will present my favorite top-10 Machine Learning YouTube Channels to follow in order to keep up with the current trends. Jeremy Howard is an Australian data scientist and entrepreneur. He is a founding researcher at fast.ai, a research institute dedicated to make Deep Learning more accessible. Prior to it, Howard was the President and Chief Scientist at Kaggle. Another useful YouTube Channel is that of Rachel Thomas, co-founder of fast.ai.